如何将预测函数与我的来自 mouse() 的汇总结果一起使用？

Question

嗨，我刚刚开始使用 R 作为学校模块的一部分。我有一个包含缺失数据的数据集，并且我使用了 mouse() 来估算缺失的数据。我现在正在尝试将 predict 函数与我的合并结果一起使用。但是，我观察到以下错误：

UseMethod("predict") 中的错误：没有适用于应用于类“c('mipo','data.frame')”的对象的“预测”方法

我在下面包含了我的整个代码，如果你们都能帮助新手，我会非常感谢它。谢谢！

```{r}
library(magrittr)
library(dplyr)
train = read.csv("Train_Data.csv", na.strings=c("","NA"))
test = read.csv("Test_Data.csv", na.strings=c("","NA"))
cols <- c("naCardiac", "naFoodNutrition", "naGenitourinary", "naGastrointestinal", "naMusculoskeletal", "naNeurological", "naPeripheralVascular", "naPain", "naRespiratory", "naSkin")
train %<>%
       mutate_each_(funs(factor(.)),cols)
test %<>%
       mutate_each_(funs(factor(.)),cols)
str(train)
str(test)
```

```{r}
library(mice)
md.pattern(train)
```

```{r}
miTrain = mice(train, m = 5, maxit = 50, meth = "pmm")
```

```{r}
model = with(miTrain, lm(LOS ~ Age + Gender + Race + Temperature + RespirationRate + HeartRate + SystolicBP + DiastolicBP + MeanArterialBP + CVP + Braden + SpO2 + FiO2 + PO2_POCT + Haemoglobin + NumWBC + Haematocrit + NumPlatelets + ProthrombinTime + SerumAlbumin + SerumChloride + SerumPotassium + SerumSodium + SerumLactate + TotalBilirubin + ArterialpH + ArterialpO2 + ArterialpCO2 + ArterialSaO2 + Creatinine + Urea + GCS + naCardiac + GCS + naCardiac + naFoodNutrition + naGenitourinary + naGastrointestinal + naMusculoskeletal + naNeurological + naPeripheralVascular + naPain + naRespiratory + naSkin))
model
summary(model)
```

```{r}
modelResults = pool(model)
modelResults
```

```{r}
pred = predict(modelResults, newdata = test)
PredTest = data.frame(test$PatientID, modelResults)
str(PredTest)
summary(PredTest)
```

Answer 1

实现这一点的一种稍微有点笨拙的方法可能是采用由fit()最终合并估计值创建的拟合模型之一并替换存储的系数。我还没有做过详细的测试，但它似乎正在处理这个简单的例子：

library(mice)

imp <- mice(nhanes, maxit = 2, m = 2)
fit <- with(data = imp, exp = lm(bmi ~ hyp + chl))
pooled <- pool(fit)

# Copy one of the fitted lm models fit to
#   one of the imputed datasets
pooled_lm = fit$analyses[[1]]
# Replace the fitted coefficients with the pooled
#   estimates (need to check they are replaced in
#   the correct order)
pooled_lm$coefficients = summary(pooled)$estimate

# Predict - predictions seem to match the
#   pooled coefficients rather than the original
#   lm that was copied
predict(fit$analyses[[1]], newdata = nhanes)
predict(pooled_lm, newdata = nhanes)

据我所知predict()，线性回归应该只取决于系数，因此您不必替换拟合模型中的其他存储值（但如果应用 以外的方法，则必须这样做predict()）。